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06/06/2023
L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference
Many recent works in simulation-based inference (SBI) rely on deep gener...
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05/30/2023
Convolutional Monge Mapping Normalization for learning on biosignals
In many machine learning applications on signals and biomedical data, es...
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01/23/2023
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation
Self-supervised learning is an increasingly popular approach to unsuperv...
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11/17/2022
Validation Diagnostics for SBI algorithms based on Normalizing Flows
Building on the recent trend of new deep generative models known as Norm...
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10/10/2022
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels
Temporal point processes (TPP) are a natural tool for modeling event-bas...
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06/29/2022
Data augmentation for learning predictive models on EEG: a systematic comparison
The use of deep learning for electroencephalography (EEG) classification...
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06/03/2022
Toward a realistic model of speech processing in the brain with self-supervised learning
Several deep neural networks have recently been shown to generate activa...
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03/11/2022
Averaging Spatio-temporal Signals using Optimal Transport and Soft Alignments
Several fields in science, from genomics to neuroimaging, require monito...
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03/02/2022
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think
Learning a parametric model of a data distribution is a well-known stati...
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02/04/2022
Deep invariant networks with differentiable augmentation layers
Designing learning systems which are invariant to certain data transform...
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12/08/2021
DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals
The quantitative analysis of non-invasive electrophysiology signals from...
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11/28/2021
Long-range and hierarchical language predictions in brains and algorithms
Deep learning has recently made remarkable progress in natural language ...
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11/15/2021
Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements
Effective characterisation of the brain grey matter cytoarchitecture wit...
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11/04/2021
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
Even though Weighted Lasso regression has appealing statistical guarante...
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10/12/2021
Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction
High-quality data accumulation is now becoming ubiquitous in the health ...
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10/12/2021
Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects
A popular approach to decompose the neural bases of language consists in...
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06/25/2021
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals
Data augmentation is a key element of deep learning pipelines, as it inf...
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05/27/2021
Robust learning from corrupted EEG with dynamic spatial filtering
Building machine learning models using EEG recorded outside of the labor...
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05/04/2021
Implicit differentiation for fast hyperparameter selection in non-smooth convex learning
Finding the optimal hyperparameters of a model can be cast as a bilevel ...
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03/03/2021
Deep Recurrent Encoder: A scalable end-to-end network to model brain signals
Understanding how the brain responds to sensory inputs is challenging: b...
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03/02/2021
Decomposing lexical and compositional syntax and semantics with deep language models
The activations of language transformers like GPT2 have been shown to li...
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02/12/2021
Leveraging Global Parameters for Flow-based Neural Posterior Estimation
Inferring the parameters of a stochastic model based on experimental obs...
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12/04/2020
Learning summary features of time series for likelihood free inference
There has been an increasing interest from the scientific community in u...
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10/22/2020
Model identification and local linear convergence of coordinate descent
For composite nonsmooth optimization problems, Forward-Backward algorith...
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09/29/2020
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso
Detecting where and when brain regions activate in a cognitive task or i...
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08/21/2020
Spectral independent component analysis with noise modeling for M/EEG source separation
Background: Independent Component Analysis (ICA) is a widespread tool fo...
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07/31/2020
Uncovering the structure of clinical EEG signals with self-supervised learning
Objective. Supervised learning paradigms are often limited by the amount...
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06/03/2020
Debiased Sinkhorn barycenters
Entropy regularization in optimal transport (OT) has been the driver of ...
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02/20/2020
Implicit differentiation of Lasso-type models for hyperparameter optimization
Setting regularization parameters for Lasso-type estimators is notorious...
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01/15/2020
Support recovery and sup-norm convergence rates for sparse pivotal estimation
In high dimensional sparse regression, pivotal estimators are estimators...
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11/13/2019
Self-supervised representation learning from electroencephalography signals
The supervised learning paradigm is limited by the cost - and sometimes ...
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10/09/2019
Spatio-Temporal Alignments: Optimal transport through space and time
Comparing data defined over space and time is notoriously hard, because ...
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10/03/2019
Multi-subject MEG/EEG source imaging with sparse multi-task regression
Magnetoencephalography and electroencephalography (M/EEG) are non-invasi...
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07/12/2019
Dual Extrapolation for Sparse Generalized Linear Models
Generalized Linear Models (GLM) form a wide class of regression and clas...
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06/04/2019
Manifold-regression to predict from MEG/EEG brain signals without source modeling
Magnetoencephalography and electroencephalography (M/EEG) can reveal neu...
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05/27/2019
Learning step sizes for unfolded sparse coding
Sparse coding is typically solved by iterative optimization techniques, ...
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02/13/2019
Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
Magnetoencephalography (MEG) and electroencephalogra-phy (EEG) are non-i...
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02/07/2019
Concomitant Lasso with Repetitions (CLaR): beyond averaging multiple realizations of heteroscedastic noise
Sparsity promoting norms are frequently used in high dimensional regress...
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01/26/2019
Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals
Convolutional dictionary learning (CDL) estimates shift invariant basis ...
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12/07/2018
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
Background: Electroencephalography (EEG) monitors brain activity during ...
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11/28/2018
Beyond Pham's algorithm for joint diagonalization
The approximate joint diagonalization of a set of matrices consists in f...
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11/06/2018
A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning
Nonnegative matrix factorization (NMF) is a popular method for audio spe...
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07/11/2018
A deep learning architecture to detect events in EEG signals during sleep
Electroencephalography (EEG) during sleep is used by clinicians to evalu...
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06/25/2018
Accelerating likelihood optimization for ICA on real signals
We study optimization methods for solving the maximum likelihood formula...
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05/25/2018
EM algorithms for ICA
Independent component analysis (ICA) is a widely spread data exploration...
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05/24/2018
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
Frequency-specific patterns of neural activity are traditionally interpr...
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05/20/2018
Wasserstein regularization for sparse multi-task regression
Two important elements have driven recent innovation in the field of reg...
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11/29/2017
Faster ICA under orthogonal constraint
Independent Component Analysis (ICA) is a technique for unsupervised exp...
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10/24/2017
A hierarchical Bayesian perspective on majorization-minimization for non-convex sparse regression: application to M/EEG source imaging
Majorization-minimization (MM) is a standard iterative optimization tech...
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07/05/2017
